Unlike mini-ImageNet, for which researchers tend to use the same class split as [1]}, CUB doesn't have an official split. Although practitioners agree on the train/validation/test split ratio (i.e., 100/50/50), there exist many different specific class split implementations [2]}, [3]}, [4]}, [5]}, [6]}.
For our CUB experiments in the main paper, we use the same random train/validation/test split as Tang  [6]}.
Many recent open-sourced works [8]}, [9]}, [10]} use the dataset-generating codehttps://github.com/wyharveychen/CloserLookFewShot/blob/master/filelists/CUB/write_CUB_filelist.py from Chen  [4]}, using raw, non-cropped images as input. Therefore, we re-run our method on CUB under the uncropped setting using the same class split as Chen  [4]}. As shown in Table REF , FRN performance is not impacted by the choice of class split.
<TABLE>